Advertisement

An Equivalent 3D Otsu’s Thresholding Method

  • Puthipong Sthitpattanapongsa
  • Thitiwan Srinark
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)

Abstract

Due to unsatisfactory segmentation results when images contain noise by the Otsu’s thresholding method. Two-dimensional (2D) and three-dimensional (3D) Otsu’s methods thus were proposed. These methods utilize not only grey levels of pixels but also their spatial informations such as mean and median values. The 3D Otsu’s methods use both kinds of spatial information while 2D Otsu’s methods use only one. Consequently the 3D Otsu’s methods more resist to noise, but also require more computational time than the 2D ones. We thus propose a method to reduce computational time and still provide satisfactory results. Unlike the 3D Otsu’s methods, our method selects each threshold component in the threshold vector independently instead of one threshold vector. The experimental results show that our method is more robust against noise, and its computational time is very close to that of the 2D Otsu’s methods.

Keywords

Image segmentation Thresholding 3D Otsu’s method Three-dimensional histogram 

References

  1. 1.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Jour. of Electronic Imaging 13(1), 146–168 (2004)CrossRefGoogle Scholar
  2. 2.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. on Systems, Man and Cybernetics 9(1), 62–66 (1979)CrossRefGoogle Scholar
  3. 3.
    Liu, J., Li, W., Tian, Y.: Automatic thresholding of gray-level pictures using two-dimension otsu method. In: Proc. of Intl. Conf. on Circuits and Systems, China, vol. 1, pp. 325–327 (1991)Google Scholar
  4. 4.
    Gong, J., Li, L., Chen, W.: Fast recursive algorithms for two-dimensional thresholding. Pattern Recognition 31(3), 295–300 (1998)CrossRefGoogle Scholar
  5. 5.
    Ningbo, Z., Gang, W., Gaobo, Y., Weiming, D.: A fast 2d otsu thresholding algorithm based on improved histogram. In: Chinese Conf. on Pattern Recognition (CCPR), pp. 1–5 (2009)Google Scholar
  6. 6.
    Yue, F., Zuo, W.M., Wang, K.Q.: Decomposition based two-dimensional threshold algorithm for gray images. Zidonghua Xuebao/Acta Automatica Sinica 35(7), 1022–1027 (2009)CrossRefGoogle Scholar
  7. 7.
    Chen, Y., Chen, D.r., Li, Y., Chen, L.: Otsu’s thresholding method based on gray level-gradient two-dimensional histogram. In: 2nd Intl. Asia Conf. on Informatics in Control, Automation and Robotics (CAR), vol. 3, pp. 282–285 (2010)Google Scholar
  8. 8.
    Jing, X.J., Li, J.F., Liu, Y.L.: Image segmentation based on 3-d maximum between-cluster variance. Tien Tzu Hsueh Pao/Acta Electronica Sinica 31(9), 1281–1285 (2003)Google Scholar
  9. 9.
    Wang, L., Duan, H., Wang, J.: A fast algorithm for three-dimensional otsu’s thresholding method. In: IEEE Intl. Sym. on IT in Medicine and Education (ITME), pp. 136–140 (2008)Google Scholar
  10. 10.
    Dongju, L., Jian, Y.: Otsu method and k-means. In: Ninth Intl. Conf. on Hybrid Intelligent Systems (HIS), vol. 1, pp. 344–349 (2009)Google Scholar
  11. 11.
    Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Puthipong Sthitpattanapongsa
    • 1
  • Thitiwan Srinark
    • 1
  1. 1.Graphics Innovation and Vision Engineering (GIVE) Laboratory, Department of Computer Engineering, Faculty of EngineeringKasetsart UniversityBangkokThailand

Personalised recommendations